1,311 research outputs found
A SYSTEM FOR CELL PHONE ANTI-THEFT THROUGH GAIT RECOGNITION
Studies show that smartphone thefts are a significant problem in the United States. [30] With many upcoming proposals to decrease the theft-rate of such devices, investigating new techniques for preventing smartphone theft is an important area of research. The prevalence of new biometric identification techniques for smartphones has led some researchers to propose biometric anti-theft measures for such devices, similar to the current fingerprint authentication system for iOS. Gait identification, a relatively recent field of study, seems to be a good fit for anti-theft because of the non-intrusive nature of passive pattern recognition in walking. In this paper, we reproduce and extend a modern gait recognition technique proposed in Cell Phone-Based Biometrics by testing the technique outside of the laboratory on real users under everyday conditions. We propose how this technique can be applied to create an anti-theft system, and we discuss future developments that will be necessary before such research is ready to be implemented in a release-quality product. Because previous studies have also centered around the ability to differentiate between individual users from a group, we will examine the accuracy of identifying whether or not a specific user is currently using a system. The system proposed in this paper shows results as high as 91% for cross-fold accuracy for some users; however, the predictive accuracy for a single day’s results ranged from 0.8% accuracy to 92.9% accuracy, showing an unreliability that makes such a system unlikely to be useful under the pressure of real-world conditions
Vibration Alert Bracelet for Notification of the Visually and Hearing Impaired
This paper presents the prototype of an electronic vibration bracelet designed to help the visually and hearing impaired to receive and send emergency alerts. The bracelet has two basic functions. The first function is to receive a wireless signal and respond with a vibration to alert the user. The second function is implemented by pushing one button of the bracelet to send an emergency signal. We report testing on a prototype system formed by a mobile application and two bracelets. The bracelets and the application form a complete system intended to be used in retirement apartment communities. However, the system is flexible and could be expanded to add new features or to serve as a research platform for gait analysis and location services. The medical and professional potential of the proposed system is that it offers a simple, modular, and cost-effective alternative to all the existing medical devices with similar functionality currently on the market. The proposed system has an educational potential as well: it can be used as a starting point for capstone projects and demonstration purposes in schools to attract students to STEM disciplines
Non-Intrusive Gait Recognition Employing Ultra Wideband Signal Detection
A self-regulating and non-contact impulse radio ultra wideband (IR-UWB) based 3D human gait analysis prototype has been modeled and developed with the help of supervised machine learning (SML) for this application for the first time. The work intends to provide a rewarding assistive biomedical application which would help doctors and clinicians monitor human gait trait and abnormalities with less human intervention in the fields of physiological examinations, physiotherapy, home assistance, rehabilitation
success determination and health diagnostics, etc.
The research comprises IR-UWB data gathered from a number of male and female participants in both anechoic chamber and multi-path environments. In total twenty four individuals have been recruited, where twenty individuals were said to have normal gait and four persons complained of knee pain that resulted in compensated spastic walking patterns. A 3D postural model of human movements has been created from the backscattering property of the radar pulses employing understanding of spherical trigonometry and vector fields. This subjective data (height of the body areas from the ground) of an individual have been recorded and implemented to extract the gait trait from associated biomechanical activity and differentiates the lower limb movement patterns from other body areas.
Initially, a 2D postural model of human gait is presented from IR-UWB sensing phenomena employing spherical co-ordinate and trigonometry where only two dimensions such as, distance from radar and height of reflection have been determined. There are five pivotal gait parameters; step frequency, cadence, step length, walking speed, total covered distance, and body orientation which have all been measured employing radar principles and short term Fourier transformation (STFT). Subsequently, the proposed gait identification and parameter characterization has been analysed, tested and validated against popularly accepted smartphone applications with resulting variations of less than 5%. Subsequently, the spherical trigonometric model has been elevated to a 3D postural model where the prototype can determine width of motion, distance from radar, and height of reflection. Vector algebra has been incorporated with this 3D model to measure knee angles and hip angles from the extension and flexion of lower limbs to understand the gait behavior throughout the entire range of bipedal locomotion. Simultaneously, the Microsoft Kinect Xbox One has been employed during the experiment to assist in the validation process. The same vector mathematics have been implemented to the skeleton data obtained from Kinect to determine both the hip and knee angles.
The outcomes have been compared by statistical graphical approach Bland and Altman (B&A) analysis.
Further, the changes of knee angles obtained from the normal gaits have been used to train popular SMLs such as, k-nearest neighbour (kNN) and support vector machines (SVM). The trained model has subsequently been tested with the new data (knee angles extracted from both normal and abnormal gait) to assess the prediction ability of gait abnormality recognition. The outcomes have been validated through standard and wellknown statistical performance metrics with promising results found. The outcomes prove the acceptability of the proposed non-contact IR-UWB gait recognition to detect gait
Development of a Wireless Mobile Computing Platform for Fall Risk Prediction
Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research
Mobile Phone Sensors Can Discern Medication-related Gait Quality Changes in Parkinson\u27s Patients in the Home Environment
Patients with Parkinson\u27s Disease (PD) experience daytime symptom fluctuations, which result in small amplitude, slow and unstable walking during times when medication attenuates. The ability to identify dysfunctional gait patterns throughout the day from raw mobile phone acceleration and gyroscope signals would allow the development of applications to provide real-time interventions to facilitate walking performance by, for example, providing external rhythmic cues. Patients (n = 20, mean Hoehn and Yahr: 2.25) had their ambulatory data recorded and were directly observed twice during one day: once after medication abstention, (OFF) and once approximately 30 min after intake of their medication (ON). Regularized generalized linear models (RGLM), neural networks (NN), and random forest (RF) classification models were individually trained for each participant. Across all subjects, our best performing classifier on average achieved an accuracy of 92.5%. This study demonstrated that smartphone accelerometers and gyroscopes can be used to distinguish between ON versus OFF times, potentially making smartphones useful intervention tools
Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing
Human gait refers to the propulsion achieved by the effort of human limbs, a reflex progression resulting from the rhythmic reciprocal bursts of flexor and extensor activity. Several quantitative models are followed by health professionals to diagnose gait abnormality. Marker-based gait quantification is considered a gold standard by the research and health communities. It reconstructs motion in 3D and provides parameters to measure gait. But, it is an expensive and intrusive technique, limited to soft tissue artefact, prone to incorrect marker positioning, and skin sensitivity problems. Hence, markerless, swiftly deployable, non-intrusive, camera-less prototypes would be a game changing possibility, and an example is proposed here. This paper illustrates a 3D gait motion analyser employing impulse radio ultra-wide band (IR-UWB) wireless technology. The prototype can measure 3D motion and determine quantitative parameters considering anatomical reference planes. Knee angles have been calculated from the gait by applying vector algebra. Simultaneously, the model has been corroborated with the popular markerless camera based 3D motion capturing system, the Kinect sensor. Bland and Altman (B&A) statistics has been applied to the proposed prototype and Kinect sensor results to verify the measurement agreement. Finally, the proposed prototype has been incorporated with popular supervised machine learning such as, k-nearest neighbour (kNN), support vector machine (SVM) and the deep learning technique deep neural multilayer perceptron (DMLP) network to automatically recognize gait abnormalities, with promising results presented
LPcomS: Towards a Low Power Wireless Smart-Shoe System for Gait Analysis in People with Disabilities
Gait analysis using smart sensor technology is an important medical diagnostic process and has many applications in rehabilitation, therapy and exercise training. In this thesis, we present a low power wireless smart-shoe system (LPcomS) to analyze different functional postures and characteristics of gait while walking. We have designed and implemented a smart-shoe with a Bluetooth communication module to unobtrusively collect data using smartphone in any environment. With the design of a shoe insole equipped with four pressure sensors, the foot pressure is been collected, and those data are used to obtain accurate gait pattern of a patient. With our proposed portable sensing system and effective low power communication algorithm, the smart-shoe system enables detailed gait analysis. Experimentation and verification is conducted on multiple subjects with different gait including free gait. The sensor outputs, with gait analysis acquired from the experiment, are presented in this thesis
A smartphone-based architecture to detect and quantify freezing of gait in Parkinson’s disease
Introduction
The freezing of gait (FOG) is a common and highly distressing motor symptom in patients with Parkinson’s Disease
(PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness
to drug treatment.
Methods
In order to verify the acceptance of a smartphone-based architecture and its reliability at detecting FOG in real-time,
we studied 20 patients suffering from PD-related FOG. They were asked to perform video-recorded Timed Up and Go
(TUG) test with and without dual-tasks while wearing the smartphone. Video and accelerometer recordings were synchronized
in order to assess the reliability of the FOG detection system as compared to the judgement of the clinicians
assessing the videos. The architecture uses two different algorithms, one applying the Freezing and Energy Index
(Moore-Bächlin Algorithm), and the other adding information about step cadence, to algorithm 1.
Results
A total 98 FOG events were recognized by clinicians based on video recordings, while only 7 FOG events were
missed by the application. Sensitivity and specificity were 70.1% and 84.1%, respectively, for the Moore-Bächlin Algorithm,
rising to 87.57% and 94.97%, respectively, for algorithm 2 (McNemar value = 28.42; p = 0.0073).
Conclusion
Results confirm previous data on the reliability of Moore-Bächlin Algorithm, while indicating that the evolution of
this architecture can identify FOG episodes with higher sensitivity and specificity. An acceptable, reliable and easy-to-implement
FOG detection system can support a better quantification of the phenomenon and hence provide data useful to
ascertain the efficacy of therapeutic approaches
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